2022
Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
Petersen G, Shatalov J, Verma T, Brim WR, Subramanian H, Brackett A, Bahar RC, Merkaj S, Zeevi T, Staib LH, Cui J, Omuro A, Bronen RA, Malhotra A, Aboian MS. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment. American Journal Of Neuroradiology 2022, 43: 526-533. PMID: 35361577, PMCID: PMC8993193, DOI: 10.3174/ajnr.a7473.Peer-Reviewed Original ResearchConceptsMachine learning-based methodsLearning-based methodsBalanced data setData setsVector machine modelMachine learningClassification algorithmsMachine modelMachineAlgorithmData basesPrediction modelPromising resultsPrimary CNS lymphomaPrediction model study RiskRisk of biasRadiomic featuresClassifierSetCNS lymphomaWebLearningFeaturesQualitySystematic review
2021
Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review
Subramanian H, Dey R, Brim WR, Tillmanns N, Petersen G, Brackett A, Mahajan A, Johnson M, Malhotra A, Aboian M. Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review. Frontiers In Oncology 2021, 11: 788819. PMID: 35004312, PMCID: PMC8733688, DOI: 10.3389/fonc.2021.788819.Peer-Reviewed Original ResearchMachine learningIdentification of gliomasNovel machine learning methodMachine learning methodsAccuracy of algorithmsFive-fold cross validationDeep learningArtificial intelligenceGlioma imagesAlgorithm trainingNeural networkHeterogeneous datasetsLearning methodsAlgorithm testingTRIPOD criteriaNormal imagesAlgorithm developmentSame datasetAbnormal imagesDatasetLimited datasetAlgorithmSingle-institution datasetCross validationLearningNIMG-38. MEASURING ADHERENCE TO TRIPOD OF ARTIFICIAL INTELLIGENCE PAPERS IN THE GLIOMA SEGMENTATION
Tillmanns N, Lum A, Brim W, Subramanian H, Lin M, Bousabarah K, Malhotra A, cui J, Brackett A, Payabvash S, Ikuta I, Johnson M, Turowski B, Aboian M. NIMG-38. MEASURING ADHERENCE TO TRIPOD OF ARTIFICIAL INTELLIGENCE PAPERS IN THE GLIOMA SEGMENTATION. Neuro-Oncology 2021, 23: vi137-vi137. PMCID: PMC8598634, DOI: 10.1093/neuonc/noab196.537.Peer-Reviewed Original ResearchArtificial intelligence papersDeep learningArtificial intelligenceGlioma segmentationMachine learningModel performanceSegmentationNetwork descriptionMachineInclusion of informationPrediction modelLearningCritical elementsIntelligenceWebPerformanceScoring itemsKeywordsTRIPOD itemsRadiomicsItemsDatabaseInformationVocabularySearchNIMG-67. A SYSTEMATIC REVIEW ON THE DEVELOPMENT OF MACHINE LEARNING MODELS FOR DIFFERENTIATING PCNSL FROM GLIOMAS
Petersen G, Shatalov J, Brim W, Subramanian H, cui J, Johnson M, Malhotra A, Aboian M, Brackett A. NIMG-67. A SYSTEMATIC REVIEW ON THE DEVELOPMENT OF MACHINE LEARNING MODELS FOR DIFFERENTIATING PCNSL FROM GLIOMAS. Neuro-Oncology 2021, 23: vi144-vi145. PMCID: PMC8598874, DOI: 10.1093/neuonc/noab196.565.Peer-Reviewed Original ResearchMachine learningDL algorithmsApplication of MLDeep learning algorithmsConvolutional neural networkMachine learning modelsSupport vector machineRisk of overfittingArtificial intelligenceLearning algorithmML algorithmsNeural networkVector machineLearning modelLarge datasetsNovel DLInternal datasetML methodsAlgorithmAverage AUCSearch strategyDatasetPromising resultsLearningRelated termsNIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW
Tillmanns N, Lum A, Brim W, Subramanian H, Lin M, Bousabarah K, Malhotra A, cui J, Brackett A, Payabvash S, Ikuta I, Johnson M, Turowski B, Aboian M. NIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW. Neuro-Oncology 2021, 23: vi145-vi145. PMCID: PMC8598815, DOI: 10.1093/neuonc/noab196.568.Peer-Reviewed Original ResearchConvolutional neural networkSegmentation of gliomasSupport vector machineGlioma segmentationDeep learningMachine learningLikelihood of overfittingMachine Learning AlgorithmsArtificial intelligenceLearning algorithmDice scoreML algorithmsTumor segmentationNeural networkVector machineCommon algorithmsSegmentationSame datasetML methodsTCIA datasetAlgorithmData acquisitionAccuracy reportingHigh accuracyLearningOTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review
Brim W, Jekel L, Petersen G, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Mahajan A, Johnson M, Mahajan A, Aboian M. OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review. Neuro-Oncology Advances 2021, 3: iii17-iii17. PMCID: PMC8351249, DOI: 10.1093/noajnl/vdab071.067.Peer-Reviewed Original ResearchConvolutional neural networkDeep learningML algorithmsMachine Learning AlgorithmsApplication of machineClassical ML algorithmsDevelopment of machineSupport vector machine algorithmVector machine algorithmArtificial intelligenceMachine learningSearch strategyDL modelsLearning algorithmFeature extractionNeural networkMachine algorithmAverage accuracyML methodsCML algorithmAlgorithmHigh accuracyLearningMachineAccuracy
2016
Tips for the First-Year Health Sciences Librarian
Brackett A. Tips for the First-Year Health Sciences Librarian. Medical Reference Services Quarterly 2016, 35: 454-460. PMID: 27657373, DOI: 10.1080/02763869.2016.1220762.Peer-Reviewed Original Research